OpenRaiser/Intern-Atlas huggingface dataset:
Learning the image processing pipeline
Venue: arXiv cs.CV 2016
Year: 2016

Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. We explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image processing pipeline as a large collection of local linear filters. We illustrate how the method has been used to design pipelines for novel sensor architectures in consumer photography applications.
Review of Fall Detection Techniques: A Data Availability Perspective
Venue: arXiv cs.LG 2016
Year: 2016

A fall is an abnormal activity that occurs rarely; however, missing to identify falls can have serious health and safety implications on an individual. Due to the rarity of occurrence of falls, there may be insufficient or no training data available for them. Therefore, standard supervised machine learning methods may not be directly applied to handle this problem. In this paper, we present a taxonomy for the study of fall detection from the perspective of availability of fall data. The proposed taxonomy is independent of the type of sensors used and specific feature extraction/selection methods. The taxonomy identifies different categories of classification methods for the study of fall detection based on the availability of their data during training the classifiers. Then, we present a comprehensive literature review within those categories and identify the approach of treating a fall as an abnormal activity to be a plausible research direction. We conclude our paper by discussing several open research problems in the field and pointers for future research.
Undecidability of a Theory of Strings, Linear Arithmetic over Length, and String-Number Conversion
Venue: arXiv cs.LO 2016
Year: 2016

In recent years there has been considerable interest in theories over string equations, length function, and string-number conversion predicate within the formal verification, software engineering, and security communities. SMT solvers for these theories, such as Z3str2, CVC4, and S3, are of immense practical value in exposing security vulnerabilities in string-intensive programs. Additionally, there are many open decidability and complexity-theoretic questions in the context of theories over strings that are of great interest to mathematicians. Motivated by the above-mentioned applications and open questions, we study a first-order, many-sorted, quantifier-free theory $T_{s,n}$ of string equations, linear arithmetic over string length, and string-number conversion predicate and prove three theorems. First, we prove that the satisfiability problem for the theory $T_{s,n}$ is undecidable via a reduction from a theory of linear arithmetic over natural numbers with power predicate, we call power arithmetic. Second, we show that the string-numeric conversion predicate is expressible in terms of the power predicate, string equations, and length function. This second theorem, in conjunction with the reduction we propose for the undecidability theorem, suggests that the power predicate is expressible in terms of word equations and length function if and only if the string-numeric conversion predicate is also expressible in the same fragment. Such results are very useful tools in comparing the expressive power of different theories, and for establishing decidability and complexity results. Third, we provide a consistent axiomatization $Γ$ for the functions and predicates of $T_{s,n}$. Additionally, we prove that the theory $T_Γ$ , obtained via logical closure of $Γ$, is not a complete theory.
Quantitative Analysis of Saliency Models
Venue: arXiv cs.GR 2016
Year: 2016

Previous saliency detection research required the reader to evaluate performance qualitatively, based on renderings of saliency maps on a few shapes. This qualitative approach meant it was unclear which saliency models were better, or how well they compared to human perception. This paper provides a quantitative evaluation framework that addresses this issue. In the first quantitative analysis of 3D computational saliency models, we evaluate four computational saliency models and two baseline models against ground-truth saliency collected in previous work.
Interdependent Scheduling Games
Venue: arXiv cs.GT 2016
Year: 2016

We propose a model of interdependent scheduling games in which each player controls a set of services that they schedule independently. A player is free to schedule his own services at any time; however, each of these services only begins to accrue reward for the player when all predecessor services, which may or may not be controlled by the same player, have been activated. This model, where players have interdependent services, is motivated by the problems faced in planning and coordinating large-scale infrastructures, e.g., restoring electricity and gas to residents after a natural disaster or providing medical care in a crisis when different agencies are responsible for the delivery of staff, equipment, and medicine. We undertake a game-theoretic analysis of this setting and in particular consider the issues of welfare maximization, computing best responses, Nash dynamics, and existence and computation of Nash equilibria.
Model-driven Simulations for Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2016
Year: 2016

The use of simulated virtual environments to train deep convolutional neural networks (CNN) is a currently active practice to reduce the (real)data-hungriness of the deep CNN models, especially in application domains in which large scale real data and/or groundtruth acquisition is difficult or laborious. Recent approaches have attempted to harness the capabilities of existing video games, animated movies to provide training data with high precision groundtruth. However, a stumbling block is in how one can certify generalization of the learned models and their usefulness in real world data sets. This opens up fundamental questions such as: What is the role of photorealism of graphics simulations in training CNN models? Are the trained models valid in reality? What are possible ways to reduce the performance bias? In this work, we begin to address theses issues systematically in the context of urban semantic understanding with CNNs. Towards this end, we (a) propose a simple probabilistic urban scene model, (b) develop a parametric rendering tool to synthesize the data with groundtruth, followed by (c) a systematic exploration of the impact of level-of-realism on the generality of the trained CNN model to real world; and domain adaptation concepts to minimize the performance bias.
Cyber-Physical Systems Security: a Systematic Mapping Study
Venue: arXiv cs.SY 2016
Year: 2016

Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds some light on how security is actually addressed when dealing with cyber-physical systems. The provided systematic map of 118 selected studies is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia.
Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent
Venue: arXiv cs.SY 2016
Year: 2016

Continuous-time Markov decision processes are an important class of models in a wide range of applications, ranging from cyber-physical systems to synthetic biology. A central problem is how to devise a policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we present a novel approach based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. The statistical approach has several advantages over conventional approaches based on uniformisation, as it can also be applied when the model is replaced by a black box, and does not suffer from state-space explosion. The use of a stochastic gradient to guide our search considerably improves the efficiency of learning policies. We demonstrate the method on a proof-of-principle non-linear population model, showing strong performance in a non-trivial task.
RCFD: A Frequency Based Channel Access Scheme for Full Duplex Wireless Networks
Venue: arXiv cs.IT 2016
Year: 2016

Recently, several working implementations of in--band full--duplex wireless systems have been presented, where the same node can transmit and receive simultaneously in the same frequency band. The introduction of such a possibility at the physical layer could lead to improved performance but also poses several challenges at the MAC layer. In this paper, an innovative mechanism of channel contention in full--duplex OFDM wireless networks is proposed. This strategy is able to ensure efficient transmission scheduling with the result of avoiding collisions and effectively exploiting full--duplex opportunities. As a consequence, considerable performance improvements are observed with respect to standard and state--of--the--art MAC protocols for wireless networks, as highlighted by extensive simulations performed in ad hoc wireless networks with varying number of nodes.
A characterization of combinatorial demand
Venue: arXiv cs.GT 2016
Year: 2016

We prove that combinatorial demand functions are characterized by two properties: continuity and the law of demand.
Security Protocols in a Nutshell
Venue: arXiv cs.CR 2016
Year: 2016

Security protocols are building blocks in secure communications. They deploy some security mechanisms to provide certain security services. Security protocols are considered abstract when analyzed, but they can have extra vulnerabilities when implemented. This manuscript provides a holistic study on security protocols. It reviews foundations of security protocols, taxonomy of attacks on security protocols and their implementations, and different methods and models for security analysis of protocols. Specifically, it clarifies differences between information-theoretic and computational security, and computational and symbolic models. Furthermore, a survey on computational security models for authenticated key exchange (AKE) and password-authenticated key exchange (PAKE) protocols, as the most important and well-studied type of security protocols, is provided.
A Comparative Study of Algorithms for Realtime Panoramic Video Blending
Venue: arXiv cs.CV 2016
Year: 2016

Unlike image blending algorithms, video blending algorithms have been little studied. In this paper, we investigate 6 popular blending algorithms---feather blending, multi-band blending, modified Poisson blending, mean value coordinate blending, multi-spline blending and convolution pyramid blending. We consider in particular realtime panoramic video blending, a key problem in various virtual reality tasks. To evaluate the performance of the 6 algorithms on this problem, we have created a video benchmark of several videos captured under various conditions. We analyze the time and memory needed by the above 6 algorithms, for both CPU and GPU implementations (where readily parallelizable). The visual quality provided by these algorithms is also evaluated both objectively and subjectively. The video benchmark and algorithm implementations are publicly available.
On the Capacity of an Elemental Two-Way Two-Tier Network
Venue: arXiv cs.IT 2016
Year: 2016

A basic setup of a two-tier network, where two mobile users exchange messages with a multi-antenna macrocell basestation, is studied from a rate perspective subject to beamforming and power constraints. The communication is facilitated by two femtocell basestations which act as relays as there is no direct link between the macrocell basestation and the mobile users. We propose a scheme based on physical-layer network coding and compute-and-forward combined with a novel approach that solves the problem of beamformer design and power allocation. We also show that the optimal beamformers are always a convex combination of the channels between the macro- and femtocell basestations. We then establish the cut-set bound of the setup to show that the presented scheme almost achieves the capacity of the setup numerically.
Polynomial Analysis Algorithms for Free Choice Probabilistic Workflow Nets
Venue: arXiv cs.LO 2016
Year: 2016

We study Probabilistic Workflow Nets (PWNs), a model extending van der Aalst's workflow nets with probabilities. We give a semantics for PWNs in terms of Markov Decision Processes and introduce a reward model. Using a result by Varacca and Nielsen, we show that the expected reward of a complete execution of the PWN is independent of the scheduler. Extending previous work on reduction of non-probabilistic workflow nets, we present reduction rules that preserve the expected reward. The rules lead to a polynomial-time algorithm in the size of the PWN (not of the Markov decision process) for the computation of the expected reward. In contrast, since the Markov decision process of PWN can be exponentially larger than the PWN itself, all algorithms based on constructing the Markov decision process require exponential time. We report on a sample implementation and its performance on a collection of benchmarks.
Self-stabilizing Reconfiguration
Venue: arXiv cs.DC 2016
Year: 2016

Current reconfiguration techniques are based on starting the system in a consistent configuration, in which all participating entities are in their initial state. Starting from that state, the system must preserve consistency as long as a predefined churn rate of processors joins and leaves is not violated, and unbounded storage is available. Many working systems cannot control this churn rate and do not have access to unbounded storage. System designers that neglect the outcome of violating the above assumptions may doom the system to exhibit illegal behaviors. We present the first automatically recovering reconfiguration scheme that recovers from transient faults, such as temporal violations of the above assumptions. Our self-stabilizing solutions regain safety automatically by assuming temporal access to reliable failure detectors. Once safety is re-established, the failure detector reliability is no longer needed. Still, liveness is conditioned by the failure detector's unreliable signals. We show that our self-stabilizing reconfiguration techniques can serve as the basis for the implementation of several dynamic services over message passing systems. Examples include self-stabilizing reconfigurable virtual synchrony, which, in turn, can be used for implementing a self-stabilizing reconfigurable state-machine replication and self-stabilizing reconfigurable emulation of shared memory.
A Fast Order-Based Approach for Core Maintenance
Venue: arXiv cs.DB 2016
Year: 2016

Graphs have been widely used in many applications such as social networks, collaboration networks, and biological networks. One important graph analytics is to explore cohesive subgraphs in a large graph. Among several cohesive subgraphs studied, k-core is one that can be computed in linear time for a static graph. Since graphs are evolving in real applications, in this paper, we study core maintenance which is to reduce the computational cost to compute k-cores for a graph when graphs are updated from time to time dynamically. We identify drawbacks of the existing efficient algorithm, which needs a large search space to find the vertices that need to be updated, and has high overhead to maintain the index built, when a graph is updated. We propose a new order-based approach to maintain an order, called k-order, among vertices, while a graph is updated. Our new algorithm can significantly outperform the state-of-the-art algorithm up to 3 orders of magnitude for the 11 large real graphs tested. We report our findings in this paper.
A multi-objective synthesis methodology for majority/minority logic networks
Venue: arXiv cs.ET 2016
Year: 2016

New technologies such as Quantum-dot Cellular Automata (QCA), Single Electron Tunneling (SET), Tunneling Phase Logic (TPL) and all-spin logic (ASL) devices have been widely advocated in nanotechnology as a response to the physical limits associated with complementary metal oxide semiconductor (CMOS) technology in atomic scales. Some of their peculiar features are their smaller size, higher speed, higher switching frequency, lower power consumption, and higher scale integration. In these technologies, the majority (or minority) and inverter gates are employed for the production of the functions as this set of gates makes a universal set of Boolean primitives in these technologies. An important step in the generation of Boolean functions using the majority gate is reducing the number of involved gates. In this paper, a multi-objective synthesis methodology (with the objective priority of gate counts, gate levels and the number of inverter gates) is presented for finding the minimal number of possible majority gates in the synthesis of Boolean functions using the proposed Majority Specification Matrix (MSM) concept. Moreover, based on MSM, a synthesis flow is proposed for the synthesis of multi-output Boolean functions. To reveal the efficiency of the proposed method, it is compared with a meta-heuristic method, multi-objective Genetic Programing (GP). Besides, it is applied to synthesize MCNC benchmark circuits. The results are indicative of the outperformance of the proposed method in comparison to multi-objective GP method. Also, for the MCNC benchmark circuits, there is an average reduction of 10.5% in the number of levels as well as 16.8% and 33.5% in the number of majority and inverter gates, as compared to the best available method respectively.
Using network centrality measures to improve national journal classification lists
Venue: arXiv cs.DL 2016
Year: 2016

In countries like Denmark and Spain classified journal lists are now being produced and used in the calculation of nationwide performance indicators. As a result, Danish and Spanish scholars are advised to contribute to journals of high 'authority' (as in the former) or those within a high class (as in the latter). This can create a few problems. The aim of this paper is to analyse the potential use of network centrality measures to identify possible mismatches of journal categories. It analysis the Danish National Authority List and the Spanish CIRC Classification. Based on a sample of Library and Information Science publications, it analyses centrality measures that can assess on the importance of journals to given fields, correcting mismatches in these classifications. We conclude by emphasising the use of these measures to better calibrate journal classifications as we observe a general bias in these lists towards older journals. Centrality measures can allow to identify periphery-to-core journals' transitions.
A Review of Pair-wise Testing
Venue: arXiv cs.SE 2016
Year: 2016

In software testing, the large size of the input domain makes exhaustively testing the inputs a daunting and often impossible task. Pair-wise testing is a popular approach to combinatorial testing problems. This paper reviews Pair-wise testing and its history, strengths, weaknesses, and tools for generating test cases.
A structured argumentation framework for detaching conditional obligations
Venue: arXiv cs.AI 2016
Year: 2016

We present a general formal argumentation system for dealing with the detachment of conditional obligations. Given a set of facts, constraints, and conditional obligations, we answer the question whether an unconditional obligation is detachable by considering reasons for and against its detachment. For the evaluation of arguments in favor of detaching obligations we use a Dung-style argumentation-theoretical semantics. We illustrate the modularity of the general framework by considering some extensions, and we compare the framework to some related approaches from the literature.
Decoding Emotional Experience through Physiological Signal Processing
Venue: arXiv cs.HC 2016
Year: 2016

There is an increasing consensus among re- searchers that making a computer emotionally intelligent with the ability to decode human affective states would allow a more meaningful and natural way of human-computer interactions (HCIs). One unobtrusive and non-invasive way of recognizing human affective states entails the exploration of how physiological signals vary under different emotional experiences. In particular, this paper explores the correlation between autonomically-mediated changes in multimodal body signals and discrete emotional states. In order to fully exploit the information in each modality, we have provided an innovative classification approach for three specific physiological signals including Electromyogram (EMG), Blood Volume Pressure (BVP) and Galvanic Skin Response (GSR). These signals are analyzed as inputs to an emotion recognition paradigm based on fusion of a series of weak learners. Our proposed classification approach showed 88.1% recognition accuracy, which outperformed the conventional Support Vector Machine (SVM) classifier with 17% accuracy improvement. Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner. Results showed that despite the feature space dimensionality reduction from 27 to 18 features, our methodology preserved the recognition accuracy of about 85.0%. This reduction in complexity will get us one step closer towards embedding this human emotion encoder in the wireless and wearable HCI platforms.
The binary primes sequence for computational hardening of pseudorandom sequences
Venue: arXiv cs.CR 2016
Year: 2016

This paper proposes the use of the binary primes sequence to strengthen pseudorandom (PN) decimal sequences for cryptography applications. The binary primes sequence is added to the PN decimal sequence (where one can choose from many arbitrary shift values) and it is shown that the sum sequence has improved autocorrelation properties besides being computationally hard. Also, an analysis on the computational complexity is performed and it is shown that the complexity for the eavesdropper is of exponential complexity and therefore, the proposed method is an attractive procedure for cryptographic applications.
Multi-Bit Read and Write Methodologies for Diode-STTRAM Crossbar Array
Venue: arXiv cs.ET 2016
Year: 2016

Crossbar arrays using emerging non-volatile memory technologies such as Resistive RAM (ReRAM) offer high density, fast access speed and low-power. However the bandwidth of the crossbar is limited to single-bit read/write per access to avoid selection of undesirable bits. We propose a technique to perform multi-bit read and write in a diode-STTRAM (Spin Transfer Torque RAM) crossbar array. Simulation shows that the biasing voltage of half-selected cells can be adjusted to improve the sense margin during read and thus reduce the sneak path through the half-selected cells. In write operation, the half-selected cells are biased with a pulse voltage source which increases the write latency of these cells and enables to write 2-bits while keeping the half-selected bits undisturbed. Simulation results indicate biasing the half-selected cells by 700mV can enable reading as much as 512-bits while sustaining 512x512 crossbar with 2.04 years retention. The 2-bit writing requires pulsing by 50mV to optimize energy.